In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.

published:18 Feb 2014

views:237628

This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#kmeans #clusteranalysis #clustering #datascience #machinelearning
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get LifetimeAccess to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the DataScience course, you should be able to:
1. Gain insight into the 'Roles' played by a DataScientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data AnalysisLife Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. InformationArchitects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
Please write back to us at sales@edureka.co or call us at +918880862004 or 18002759730 for more information.
Website: https://www.edureka.co/data-science
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Gnana Sekhar Vangara, TechnologyLead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

published:01 Mar 2017

views:15652

published:14 Apr 2016

views:69969

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.

This video visualizes how Hartigan's algorithm approaches the problem of k-means clustering.

published:22 Dec 2015

views:820

[http://bit.ly/k-NN] K-D trees allow us to quickly find approximate nearest neighbours in a (relatively) low-dimensional real-valued space. The algorithm works by recursively partitioning the set of training instances based on a median value of a chosen attribute. When we get a new data instance, we find the matching leaf of the K-D tree, and compare the instance to all the training point in that leaf.

There will be a continuation of K-Means to Gaussian MixtureModels and EM.

published:26 Mar 2017

views:210

published:24 Jul 2015

views:4668

K-Means Algorithm for clustering by Gaurav Vohra, founder of JigsawAcademy. This is a clip from the Clustering module of our course on analytics.
Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

An algorithm is an effective method that can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function. Starting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, eventually producing "output" and terminating at a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input.

Raw data, i.e. unprocessed data, is a collection of numbers, characters; data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next. Field data is raw data that is collected in an uncontrolled in situ environment. Experimental data is data that is generated within the context of a scientific investigation by observation and recording.

The Latin word "data" is the plural of "datum", and still may be used as a plural noun in this sense. Nowadays, though, "data" is most commonly used in the singular, as a mass noun (like "information", "sand" or "rain").

Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties.

This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#kmeans #clusteranalysis #clustering #datascience #machinelearning
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get LifetimeAccess to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the DataScience course, you should be able to:
1. Gain insight into the 'Roles' played by a DataScientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data AnalysisLife Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. InformationArchitects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
Please write back to us at sales@edureka.co or call us at +918880862004 or 18002759730 for more information.
Website: https://www.edureka.co/data-science
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Gnana Sekhar Vangara, TechnologyLead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

12:50

The k Means Algorithm | Stanford University

The k Means Algorithm | Stanford University

The k Means Algorithm | Stanford University

7:54

EM algorithm: how it works

EM algorithm: how it works

EM algorithm: how it works

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.

Hartigan's algorithm for k-means clustering

This video visualizes how Hartigan's algorithm approaches the problem of k-means clustering.

3:09

KD tree algorithm: how it works

KD tree algorithm: how it works

KD tree algorithm: how it works

[http://bit.ly/k-NN] K-D trees allow us to quickly find approximate nearest neighbours in a (relatively) low-dimensional real-valued space. The algorithm works by recursively partitioning the set of training instances based on a median value of a chosen attribute. When we get a new data instance, we find the matching leaf of the K-D tree, and compare the instance to all the training point in that leaf.

Machine Learning Algorithms - K-Means Clustering Algorithm

There will be a continuation of K-Means to Gaussian MixtureModels and EM.

5:08

The Lloyd Algorithm for k-Means Clustering

The Lloyd Algorithm for k-Means Clustering

The Lloyd Algorithm for k-Means Clustering

5:30

Technical Course: Cluster Analysis: K-Means Algorithm for Clustering

Technical Course: Cluster Analysis: K-Means Algorithm for Clustering

Technical Course: Cluster Analysis: K-Means Algorithm for Clustering

K-Means Algorithm for clustering by Gaurav Vohra, founder of JigsawAcademy. This is a clip from the Clustering module of our course on analytics.
Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

30:56

K-Means Clustering - The Math of Intelligence (Week 3)

K-Means Clustering - The Math of Intelligence (Week 3)

K-Means Clustering - The Math of Intelligence (Week 3)

Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this.
Code for this video:
https://github.com/llSourcell/k_means_clustering
Please Subscribe! And like. And comment. That's what keeps me going.
More learning resources:
http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html
http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html
http://people.revoledu.com/kardi/tutorial/kMean/
https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html
http://mnemstudio.org/clustering-k-means-example-1.htm
https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial
http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html
Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/
And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajologyyy
Instagram: https://www.instagram.com/llsourcell/

EEE6512 - Image Segmentation using K-means Clustering Algorithm

k means clustering algorithm

This video contains detailed explanation about k-means clustering algorithm with each step explained along with a primary conclusion.
Please Like and Subscribe !!!!
For more videos visit InnovationHeightsI.T/Data Mining Numericals Playlist.

This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#kmeans #clusteranalysis #clustering #datascience #machinelearning
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have...

published: 01 Mar 2017

The k Means Algorithm | Stanford University

published: 14 Apr 2016

EM algorithm: how it works

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.

Hartigan's algorithm for k-means clustering

This video visualizes how Hartigan's algorithm approaches the problem of k-means clustering.

published: 22 Dec 2015

KD tree algorithm: how it works

[http://bit.ly/k-NN] K-D trees allow us to quickly find approximate nearest neighbours in a (relatively) low-dimensional real-valued space. The algorithm works by recursively partitioning the set of training instances based on a median value of a chosen attribute. When we get a new data instance, we find the matching leaf of the K-D tree, and compare the instance to all the training point in that leaf.

Machine Learning Algorithms - K-Means Clustering Algorithm

There will be a continuation of K-Means to Gaussian MixtureModels and EM.

published: 26 Mar 2017

The Lloyd Algorithm for k-Means Clustering

published: 24 Jul 2015

Technical Course: Cluster Analysis: K-Means Algorithm for Clustering

K-Means Algorithm for clustering by Gaurav Vohra, founder of JigsawAcademy. This is a clip from the Clustering module of our course on analytics.
Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

published: 02 Apr 2012

K-Means Clustering - The Math of Intelligence (Week 3)

Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this.
Code for this video:
https://github.com/llSourcell/k_means_clustering
Please Subscribe! And like. And comment. That's what keeps me going.
More learning resources:
http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html
http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html
http://people.revoledu.com/kardi/tutorial/kMean/
https://home.deib.pol...

EEE6512 - Image Segmentation using K-means Clustering Algorithm

k means clustering algorithm

This video contains detailed explanation about k-means clustering algorithm with each step explained along with a primary conclusion.
Please Like and Subscribe !!!!
For more videos visit InnovationHeightsI.T/Data Mining Numericals Playlist.

This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introducti...

This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#kmeans #clusteranalysis #clustering #datascience #machinelearning
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get LifetimeAccess to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the DataScience course, you should be able to:
1. Gain insight into the 'Roles' played by a DataScientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data AnalysisLife Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. InformationArchitects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
Please write back to us at sales@edureka.co or call us at +918880862004 or 18002759730 for more information.
Website: https://www.edureka.co/data-science
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Gnana Sekhar Vangara, TechnologyLead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#kmeans #clusteranalysis #clustering #datascience #machinelearning
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get LifetimeAccess to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the DataScience course, you should be able to:
1. Gain insight into the 'Roles' played by a DataScientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data AnalysisLife Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. InformationArchitects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
Please write back to us at sales@edureka.co or call us at +918880862004 or 18002759730 for more information.
Website: https://www.edureka.co/data-science
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Gnana Sekhar Vangara, TechnologyLead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

EM algorithm: how it works

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sourc...

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.

[http://bit.ly/k-NN] K-D trees allow us to quickly find approximate nearest neighbours in a (relatively) low-dimensional real-valued space. The algorithm works by recursively partitioning the set of training instances based on a median value of a chosen attribute. When we get a new data instance, we find the matching leaf of the K-D tree, and compare the instance to all the training point in that leaf.

[http://bit.ly/k-NN] K-D trees allow us to quickly find approximate nearest neighbours in a (relatively) low-dimensional real-valued space. The algorithm works by recursively partitioning the set of training instances based on a median value of a chosen attribute. When we get a new data instance, we find the matching leaf of the K-D tree, and compare the instance to all the training point in that leaf.

K-Means Algorithm for clustering by Gaurav Vohra, founder of JigsawAcademy. This is a clip from the Clustering module of our course on analytics.
Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

K-Means Algorithm for clustering by Gaurav Vohra, founder of JigsawAcademy. This is a clip from the Clustering module of our course on analytics.
Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

K-Means Clustering - The Math of Intelligence (Week 3)

Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning f...

Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this.
Code for this video:
https://github.com/llSourcell/k_means_clustering
Please Subscribe! And like. And comment. That's what keeps me going.
More learning resources:
http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html
http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html
http://people.revoledu.com/kardi/tutorial/kMean/
https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html
http://mnemstudio.org/clustering-k-means-example-1.htm
https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial
http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html
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Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this.
Code for this video:
https://github.com/llSourcell/k_means_clustering
Please Subscribe! And like. And comment. That's what keeps me going.
More learning resources:
http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html
http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html
http://people.revoledu.com/kardi/tutorial/kMean/
https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html
http://mnemstudio.org/clustering-k-means-example-1.htm
https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial
http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html
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http://wizards.herokuapp.com/
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k means clustering algorithm

This video contains detailed explanation about k-means clustering algorithm with each step explained along with a primary conclusion.
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This video contains detailed explanation about k-means clustering algorithm with each step explained along with a primary conclusion.
Please Like and Subscribe !!!!
For more videos visit InnovationHeightsI.T/Data Mining Numericals Playlist.

This video contains detailed explanation about k-means clustering algorithm with each step explained along with a primary conclusion.
Please Like and Subscribe !!!!
For more videos visit InnovationHeightsI.T/Data Mining Numericals Playlist.

This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
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Check our complete Data Science playlist here: https://goo.gl/60NJJS
#kmeans #clusteranalysis #clustering #datascience #machinelearning
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1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
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About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
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Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
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9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
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The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
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EM algorithm: how it works

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.

KD tree algorithm: how it works

[http://bit.ly/k-NN] K-D trees allow us to quickly find approximate nearest neighbours in a (relatively) low-dimensional real-valued space. The algorithm works by recursively partitioning the set of training instances based on a median value of a chosen attribute. When we get a new data instance, we find the matching leaf of the K-D tree, and compare the instance to all the training point in that leaf.

14:19

(ML 1.6) k-Nearest Neighbor classification algorithm

Description of kNN.
A playlist of these Machine Learning videos is available here:
h...

Technical Course: Cluster Analysis: K-Means Algorithm for Clustering

K-Means Algorithm for clustering by Gaurav Vohra, founder of JigsawAcademy. This is a clip from the Clustering module of our course on analytics.
Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training
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30:56

K-Means Clustering - The Math of Intelligence (Week 3)

Let's detect the intruder trying to break into our security system using a very popular ML...

K-Means Clustering - The Math of Intelligence (Week 3)

Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this.
Code for this video:
https://github.com/llSourcell/k_means_clustering
Please Subscribe! And like. And comment. That's what keeps me going.
More learning resources:
http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html
http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html
http://people.revoledu.com/kardi/tutorial/kMean/
https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html
http://mnemstudio.org/clustering-k-means-example-1.htm
https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial
http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html
Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/
And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajologyyy
Instagram: https://www.instagram.com/llsourcell/

k means clustering algorithm...

k mean clustering algorithm شرح...

How K-Means algorithm works...

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